Occlusions present a significant challenge for connected and automated vehicles, as they can obscure critical road users from perception systems. Traditional risk metrics often fail to capture the cumulative nature of these threats over time adequately. In this paper, we propose a novel and universal risk assessment metric, the Risk of Tracking Loss (RTL), which aggregates instantaneous risk intensity throughout occluded periods. This provides a holistic risk profile that encompasses both high-intensity, short-term threats and prolonged exposure. Utilizing diverse and high-fidelity real-world datasets, a large-scale statistical analysis is conducted to characterize occlusion risk and validate the effectiveness of the proposed metric. The metric is applied to evaluate different vehicle-to-everything (V2X) deployment strategies. Our study shows that full V2X penetration theoretically eliminates this risk, the reduction is highly nonlinear; a substantial statistical benefit requires a high penetration threshold of 75–90%. To overcome this limitation, we propose a novel asymmetric communication framework that allows even non-connected vehicles to receive warnings. Experimental results demonstrate that this paradigm achieves better risk mitigation performance. We found that our approach at 25% penetration outperforms the traditional symmetric model at 75%, and benefits saturate at only 50% penetration. This work provides a crucial risk assessment metric and a cost-effective, strategic roadmap for accelerating the safety benefits of V2X deployment.

Cooperative perception presents significant potential for enhancing the sensing capabilities of individual vehicles, however, inter-agent latency remains a critical challenge. Latencies cause misalignments in both spatial and semantic features, complicating the fusion of real-time observations from the ego vehicle with delayed data from others. To address these issues, we propose TraF-Align, a novel framework that learns the flow path of features by predicting the feature-level trajectory of objects from past observations up to the ego vehicle’s current time. By generating temporally ordered sampling points along these paths, TraF-Align directs attention from the current-time query to relevant historical features along each trajectory, supporting the reconstruction of current-time features and promoting semantic interaction across multiple frames. This approach corrects spatial misalignment and ensures semantic consistency across agents, effectively compensating for motion and achieving coherent feature fusion. Experiments on two real-world datasets, V2V4Real and DAIR-V2X-Seq, show that TraF-Align sets a new benchmark for asynchronous cooperative perception. The code is available at https://github.com/zhyingS/TraF-Align.

For integrated sensing and communication (ISAC) systems, channel information that is essential for communication and sensing tasks fluctuates at different timescales. Specifically, the composite channel state information (CSI) for wireless communication is static during channel coherence time. However, this concept is less appropriate for describing the wireless channel for sensing. To this end, in this paper, we first introduce a new timescale to study the real-time variations of the path state information (PSI) (e.g., delay, angle, and Doppler) of individual multi-path, termed path-invariant time, during which the PSI remains constant. As the goal of environment sensing for PSI essentially aligns with the channel estimation for the recently proposed delay-Doppler alignment modulation (DDAM) technique, we introduce a novel framework for a bi-static ISAC system, which refers to as DDAM-based ISAC. To acquire the PSI, in this paper, by capitalizing on the dual timescales of wireless channels, we propose a novel algorithm, termed as adaptive simultaneously orthogonal matching pursuit algorithm with support refinement (ASOMP-SR). The performance of DDAM with the imperfectly sensed PSI is analyzed, where the signal-to-interference-plus-noise ratio (SINR) and the achievable spectral efficiency are derived. Numerical results unveil that the proposed ASOMP-SR algorithm achieves better sensing performance than the conventional orthogonal matching pursuit (OMP) algorithm, in terms of the normalized mean squared error (NMSE) and the number of multi-paths resolved. In addition, DDAM-based ISAC can achieve superior spectral efficiency and a reduced peak-to-average power ratio (PAPR) compared to standard orthogonal frequency division multiplexing (OFDM).

Online relative pose estimation within constrained time frame is a critical challenge for object-level multi-agent cooperative perception. Specifically, its objective is to determine the relative translation and rotation of cooperating agents such that the detected objects are aligned. Current methodologies adopt a non-probabilistic approach to data association and a singular association hypothesis is assumed, resulting in overconfident pose estimates and diminished accuracy in ambiguous environments. A probabilistic relative pose estimation approach is proposed to directly address this limitation by jointly considering all the potential association hypotheses and their respective likelihoods. We construct a comprehensive Bayesian estimation problem encompassing data association and pose inference. An iterative message-passing algorithm is employed on the proposed factor graph to derive near-optimal and real-time relative pose estimates. Numerical studies verify the real-time performance and effectiveness of the proposed method.

Lane detection is essential for automated driving and advanced driver assistance systems (ADAS), including functions like lane-keeping, lane departure warning, and forward collision warning. Nevertheless, adverse weather conditions such as heavy rain or snow can substantially affect the effectiveness of lane detection systems, posing challenges in real-world scenarios. To address these challenges, we proposed an image augmentation strategy for the Cross-Layer Refinement Network-based lane detection system in adverse weather conditions. The benefits of the augmentation strategy are evaluated by training and testing the models with and without augmentation. The proposed method outperforms the non-augmented model on well-established benchmark datasets such as CULane and TuSimple. As shown in the experimental results, 12.1% enhancement in average F1@50 accuracy and 6.3% improvement in F1 accuracy are achieved. This confirms the effectiveness of the augmented cross-layer refinement network in achieving robust lane detection performance under various adverse weather conditions.

Vehicle-to-vehicle (V2V) communication is vital in developing connected and automated driving. Currently, Sub6GHz stands out as the primary frequency band in use, with the anticipation that millimeter wave (mmWave) will become a crucial component in future V2V communication. Maintaining a clear line-of-sight path is essential for effective communication. However, the vehicles between the transmitting and the receiving vehicles can obstruct the line-of-sight paths and consequently impact the performance of V2V communication. Hence, this paper introduces a geometry-based Fresnel non-line-of-sight probability model for mmWave V2V communications. The model is designed to address highway multi-lane scenarios across different traffic conditions. Factors like traffic density, vehicle size, and different traffic flow compositions are considered.

Integrated sensing and communication is a promising technology for enabling high-speed and low-latency commu-nication between connected vehicles. However, due to the unique characteristics of millimeter wave (mmWave) communication, such as severe path loss, susceptibility to blockage and beam alignment mismatch caused by positioning or sensing errors, outage probability analysis is critical to evaluate the performance of such systems. In this paper, we investigate the outage probability of mm Wave V2V communication systems in highway and urban scenarios with varying propagation environments and traffic densities. Furthermore, the optimal 3dB beamwidth is also derived for a given transmitting power and communication distance.

For integrated sensing and communication (ISAC) systems, the desired channel variables by communication and sensing tasks vary with different timescales. For sensing, one is mainly interested in the state information (e.g., delays, angles, Doppler frequencies, etc.) of individual multi-path channel components, which evolves much more slowly than the composite channel state information (CSI) required by communications. In this paper, by exploiting the double timescales for sensing and communication, a novel technique termed as delay-Doppler alignment modulation (DDAM) is investigated, which is an appealing technique for ISAC systems, since the sensing result of resolvable multi-paths can be directly exploited for delay-Doppler compensation and path-based beamforming of DDAM. We first show that with perfect CSI, as long as the number of base station (BS) antennas is no smaller than that of resolvable multi-paths, the proposed DDAM is able to transform the time-frequency double selective-fading channel into a simple additive white Gaussian noise (AWGN) channel for inter-symbol interference (ISI)-free communication without requiring the conventional channel equalization or multi-carrier transmission. We then present the DDAM-based signal processing for ISAC, and the resulting communication performance with imperfectly sensed CSI is studied. Simulation results demonstrate that the proposed DDAM-based ISAC can achieve higher communication rate compared to orthogonal frequency division multiplexing (OFDM) and DFT-spread(s)-OFDM, while guaranteeing high sensing performance.

Cooperative perception is challenging for safety-critical autonomous driving applications. The errors in the shared position and pose cause an inaccurate relative transform estimation and disrupt the robust mapping of the Ego vehicle. We propose a distributed object-level cooperative perception system called OptiMatch, in which the detected 3D bounding boxes and local state information are shared between the connected vehicles. To correct the noisy relative transform, the local measurements of both connected vehicles (bounding boxes) are utilized, and an optimal transport theory-based algorithm is developed to filter out those objects jointly detected by the vehicles along with their correspondence, constructing an associated co-visible set. A correction transform is estimated from the matched object pairs and further applied to the noisy relative transform, followed by global fusion and dynamic mapping. Experiment results show that robust performance is achieved for different levels of location and heading errors, and the proposed framework outperforms the state-of-the-art benchmark fusion schemes, including early, late, and intermediate fusion, on average precision by a large margin when location and/or heading errors occur.

Passive localization is an important aspect of integrated sensing and communication (ISAC). However, it is challenging to estimate the target position and velocity accurately from the receiving signals due to complex multipath propagation. This article presents TDLoc, a multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM)-based passive localization and tracking system, using channel state information (CSI). We first proposed a fourth-order tensor model that contains the Angle-of-Departure (AoD), Angle-of-Arrival (AoA), Time-of-Flight (ToF), and Doppler frequency shifts (DFSs) information, followed by developing an efficient joint estimation algorithm. We also show that with more than one pair of transceivers, our method can obtain the target velocity from the relativistic Doppler effects, leading to additional DFS-based trajectory information. Moreover, the Cramér–Rao lower bound (CRLB) for multipath parameter estimation and positioning is derived for performance evaluation. Numerical results show that TDLoc outperforms state-of-the-art methods in terms of localization accuracy.

We propose a sensing-assisted dynamic beamforming method for robust vehicle-to-vehicle (V2V) communication between neighboring vehicles. For the proposed method, the main beam is directly steered towards the intended receivers and searching steps are not required. To maximize transmission throughput and avoid connection interruption caused by sensing uncertainties, higher directional gain for the interested field-of-view and lower sidelobe are expected. The problem is formulated as an array pattern synthesis problem that can be solved efficiently with the widely used semi-definite relaxation (SDR) methods.

5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach.

5G millimeter wave (mmWave) signals can be used to jointly localize the receiver and map the propagation environment in vehicular networks, which is a typical simultaneous localization and mapping (SLAM) problem. Mapping the environment is challenging, due to measurements comprising both specular and diffuse multipath components, where diffuse multipath is usually considered as a perturbation. We here propose a novel method to utilize all available multipath signals from each landmark for mapping and incorporate this into a Poisson multi-Bernoulli mixture for the 5G SLAM problem. Simulation results demonstrate the efficacy of the proposed scheme.

Beamspace processing is an efficient and commonly used approach in harmonic retrieval (HR). In the beamspace, measurements are obtained by linearly transforming the sensing data, thereby achieving a compromise between estimation accuracy and system complexity. Meanwhile, the widespread use of multi-sensor technology in HR has highlighted the necessity to move from a matrix (two-way) to tensor (multi-way) analysis. In this paper, we propose a beamspace tensor-ESPRIT for multidimensional HR. In our algorithm, parameter estimation and association are achieved simultaneously.

Collaborative localization over vehicular networks is challenging if quality varies among the collected multiple sources of information. These information sources are either from vehicle on-board sensors or remote sensing using vehicular communications. The variation in the quality of remote sensor information may cause estimation performance deterioration, even threatening the system security. In this paper, we propose a distributed localization framework with truth discovery for heterogeneous and dynamic vehicular networks. Firstly, it allows vehicles to learn which neighboring vehicles they should cooperate with. Secondly, it is resilient against the quality variation of the shared information between the connected vehicles.

Location-aware communication is one of the enabling techniques for future 5G networks. It requires accurate temporal and spatial channel estimation from multidimensional data. Most of the existing channel estimation techniques assume that the measurements are complete and noise is Gaussian. While these approaches are brittle to corrupted or outlying measurements, which are ubiquitous in real applications. To address these issues, we develop a lp-norm minimization based iteratively reweighted higher-order singular value decomposition algorithm. It is robust to Gaussian as well as the impulsive noise even when the measurement data is incomplete. Compared with the state-of-the-art techniques, accurate estimation results are achieved for the proposed approach.

We propose a search-free beamspace tensor-ESPRIT algorithm for millimeter wave MIMO channel estimation. It is a multidimensional generalization of beamspace-ESPRIT method by exploiting the multiple invariance structure of the measurements. Geometry-based channel model is considered to contain the channel sparsity feature. In our framework, an alternating least squares problem is solved for low rank tensor decomposition and the multidimensional parameters are automatically associated. The performance of the proposed algorithm is evaluated by considering different transformation schemes.

5G new radio (NR) provides new opportunities for accurate positioning from a single reference station: large bandwidth combined with multiple antennas, at both the base station and user sides, allows for unparalleled angle and delay resolution. Nevertheless, positioning quality is affected by multipath and clock biases. We study, in terms of performance bounds and algorithms, the ability to localize a vehicle in the presence of multipath and unknown user clock bias. We find that when a sufficient number of paths is present, a vehicle can still be localized thanks to redundancy in the geometric constraints. Moreover, the 5G NR signals enable a vehicle to build up a map of the environment.

We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic. We show that these approaches do not perform well when the attacker has a limited number of data samples only. We design an enhanced algorithm to construct FDI attack vectors in the face of limited measurements that can nevertheles bypass the BDD with high probability. Furthermore, we characterize an important trade-off between the attack's BDD-bypass probability and its sparsity, which affects the spatial extent of the attack that must be achieved. Extensive simulations using data traces collected from the MATPOWER simulator and benchmark IEEE bus systems validate our findings.

Multipath-assisted positioning makes use of specular multipath components (MPCs), whose parameters are geometrically related to the positions of the transceiver nodes. Diffuse scattering from rough surfaces affects the observed specular reflections in the angular and delay domains. Based on the effective roughness approach, the angular delay power spectrum can be calculated as a function of location parameters, which-in a next step-could be useful to accurately characterize the position-related information of MPCs. The calculated power spectra follow reported characteristics of stochastic multipath models, i.e. Gaussian shape in the angular domain and an exponential shape in the delay domain. The resulting angular and delay spreads are in an equivalent range to values reported in literature.

We propose a diffusion expectation-maximization algorithm with adaptive combiner for distributed estimation over sensor networks. Due to the spatial distribution of the nodes, variation of node profile across the network is a common phenomena in real applications. The unreliable nodes exist and provide inaccurate estimates, which may be caused by high levels of noise or malicious attacks. Instead of using a static combiner, an efficient adaptive combination scheme is developed by formulating it as a ℓ0-norm regularized minimum variance unbiased estimation problem. The proposed algorithm is robust to the variation of node profile across the network. Furthermore, it can be extended for node-specific processing. Each node estimates a subset of the global variable of the whole network.

An efficient subspace-based two-step direction finding method is proposed for uniform linear arrays. It improves the estimation accuracy for small sample size and coherent sources by diminishing the undesirable terms and utilizing the Toeplitz structure of the sample covariance matrix. Furthermore, it works well even using single snapshot, therefore, it is a good candidate to track the direction-of-arrival of fast moving targets. The performance of the proposed technique is evaluated over numerical studies in terms of root mean square error. Computer simulations show that the robust scheme outperforms several state-of-the-art methods.

Higher-order singular value decomposition (HOSVD) is usually required in$R$-dimensional ($R$-D) harmonic retrieval, where$R \geq 3$. In this letter, we devise an iteratively reweighted HOSVD technique, which is referred to as IR-HOSVD, for multi-dimensional frequency estimation in the presence of impulsive noise. The main idea is to minimize the${\ell _p}$-norm residual errors along all the$R$dimensions, where$1 < p < 2$. After decomposition, standard subspace techniques can be applied for parameter estimation. Based on the numerical results, IR-HOSVD outperforms several state-of-the-art techniques in terms of root mean square frequency error for different impulsive noise models.